The statements in this section merely provide background information related to the present disclosure. Accordingly, such statements are not intended to constitute an admission of prior art.
Emissions control is an important factor in engine design and engine control. One particular combustion by-product, NOx, is created by nitrogen and oxygen molecules present in engine intake air disassociating in the high temperatures of combustion. Rates of NOx creation include known relationships to the combustion process, for example, with higher rates of NOx creation being associated with higher combustion temperatures and longer exposure of air molecules to the higher temperatures. Reduction of NOx created in the combustion process and management of NOx in an exhaust aftertreatment system are priorities in vehicle design.
NOx molecules, once created in the combustion chamber, can be converted back into nitrogen and oxygen molecules in exemplary devices known in the art within the broader category of aftertreatment devices. However, one having ordinary skill in the art will appreciate that aftertreatment devices are largely dependent upon operating conditions, such as device operating temperature driven by exhaust gas flow temperatures.
Modern engine control methods utilize diverse operating strategies to optimize combustion. Some operating strategies, optimizing combustion in terms of fuel efficiency, include lean, localized, or stratified combustion within the combustion chamber in order to reduce the fuel charge necessary to achieve the work output required of the cylinder. While temperatures in the combustion chamber can get high enough in pockets of combustion to create significant quantities of NOx, the overall energy output of the combustion chamber, in particular, the heat energy expelled from the engine through the exhaust gas flow, can be greatly reduced from normal values. Such conditions can be challenging to exhaust aftertreatment strategies, since, as aforementioned, aftertreatment devices frequently require an elevated operating temperature, driven by the exhaust gas flow temperature, to operate adequately to treat NOx emissions.
Aftertreatment devices are known, for instance, utilizing catalysts capable of storing some amount of NOx, and engine control technologies have been developed to combine these NOx traps or NOx adsorbers with fuel efficient engine control strategies to improve fuel efficiency and still achieve acceptable levels of NOx emissions. One exemplary strategy includes using a NOx trap to store NOx emissions during fuel lean operations and then purging the stored NOx during fuel rich, higher temperature engine operating conditions with conventional three-way catalysis to nitrogen and water. Such purging events or regeneration events can be the result of changing vehicle operation or forced purging events. A forced purging event requires monitoring the amount of NOx stored and some mechanism or criteria to initiate the purge. For example, a NOx trap has a limited storage capacity, and sensors can be used in the exhaust gas flow to estimate NOx creation in order to estimate the NOx trap state. Once the NOx trap gets close to its full capacity, it must be regenerated with a fuel rich reducing “pulse”. It is desirable to control the efficiency of the regeneration event of the NOx trap to provide optimum emission control and minimum fuel consumption. Various strategies have been proposed.
Techniques are known for adsorbing NOx (trapping) when the air-fuel ratio of the exhaust gas flowing into the NOx adsorbent is lean and releasing the adsorbed NOx (regenerating) when the air-fuel ratio of the exhaust gas flowing into the NOx adsorbent becomes rich wherein the amount of NOx adsorbed in the NOx adsorbent may be estimated from the engine load and the engine rotational speed. When the amount of the estimated NOx becomes the maximum NOx adsorption capacity of the NOx adsorbent, the air-fuel ratio of the exhaust gas flowing into the NOx adsorbent is made rich. Determination of a regeneration phase may also be on the basis of individual operating cycles of the internal combustion engine.
It is also known to estimate how full the NOx trap is by estimating the amount of NOx flowing into the NOx trap using a NOx sensor or a pre-NOx trap oxygen sensor. It is also known to schedule regeneration based on estimations of accumulated NOx mass and engine load and speed operating condition probabilities.
Increasingly stringent emission standards require NOx aftertreatment methods, utilizing, for example, a selective catalytic reduction device (SCR). An SCR utilizes ammonia derived from urea injection or recovered from normal operation of a three-way catalyst device to treat NOx. Continued improvement in exhaust aftertreatment requires accurate information regarding NOx emissions in the exhaust gas flow in order to achieve effective NOx reduction, such as dosing proper amount of urea based on monitored NOx emissions.
A NOx sensor or an oxygen sensor add cost and weight to a vehicle, and such sensors frequently require a particular operating temperature range, achieved after some warm-up time, to be functional. There exist methods to estimate engine-out NOx via detailed combustion modeling using heat release model, multi-zone combustion model and Zodovich chemical kinetic equations. This detailed modeling, although good for analysis, may not be appropriate for in-vehicle engine control module (ECM) applications because of complicated programming and calibration requirements. Additionally, such models are sensitive to sensor tolerance and aging, pose a large computational burden upon the ECM, and require processing time not providing results in real-time.
A combustion model predicting NOx creation from combustion parameters must take into account all of the variable parameters that may occur within a vehicle. While it might be possible for a technician to individually analyze and design a custom algorithm for each vehicle and periodically tune the algorithm to changing system and operating conditions, it would be unwieldy to perform such operations on a wide spread basis. It is instead preferable that some automatic control monitors the system and adjusts parameters of the control algorithm on the basis of the performance of the specific system. Machine learning algorithms have been developed to allow automated adjustment of functional mechanisms on the basis of changing conditions and results. A number of different machine learning algorithm techniques have become widely explored; one of particular application to the present disclosure includes a neural network.
Neural networks are well known in the art and will not be described in detail herein. However, as is most relevant to this disclosure, artificial neural networks or neural networks are computer systems created to emulate biological means of decision making. Whereas traditional computing means are based upon sequential processing of data through an algorithm yielding predictable results, neural networks are known to process data in consecutive layers and parallel paths within each layer through alternate nodes. The neural network is initially trained with data yielding a known set of results. As a result of this training, weights are applied between the layers and among the nodes, the network automatically adapting to the training data and adjusting the weights to more closely model the data. In later use, the neural network can retain the training adjustments and apply them through the life of the network, or the network can employ various known methods to learn from ongoing data patterns. Neural networks have the benefit of being adaptive to complex data sets and changing conditions. Whereas traditional algorithms must be programmed with a fixed functional process, attempting to anticipate all possible operational permutations of the system at the time of the creation of the algorithm, neural networks can be used in situations where not all of the factors or relationships in the data are known at the time of the creation of the network.
A method estimating NOx creation in a combustion process, combining the real-time effectiveness of a NOx sensor with the cost and weight efficiency of a model based NOx estimation would be advantageous.